2022 is shaping up to mark a significant shift in how credit unions run their lending operations. In a recent survey of credit union professionals attending the NACUSO conference last month, 61% said their credit union’s underwriting technology is outdated. Eight out of ten said they believe increased use of artificial intelligence (AI) and machine learning (ML) would lead to better credit scoring. And 67% said that AI underwriting is an investment priority for 2022.
Credit unions are focused on serving their communities and increasing loan volume to their members. Many of them are doing this by shrugging off the limited perspective offered by FICO scores in favor of a much wider view of their members. They’re using AI and ML technology to analyze significantly more data for loan underwriting, so they can say “yes” to more people, while reducing risk and providing greater access to previously underserved groups.
As this transition takes place, many credit unions are recognizing some major misconceptions in how they previously assessed lending risk – misconceptions that can impact both their bottom line and their ability to serve their communities.
What You Didn’t Know About Income and Risk
Historically, the correlation between income and credit risk has appeared quite straightforward. People with higher incomes are less likely to default on a loan and therefore are less risky than applicants with lower incomes. After all, a member with more money is more likely to use that money to pay off their loan in a timely fashion. Right?
Not quite – there are more factors at work.
The relationship between income and risk is not as clear-cut as we were once taught, and it is because of income’s relationships to other variables, especially credit history. Our data shows that people with less credit history and higher reported income present a high level of risk. We also noticed that people with high income who had their salaries directly deposited into their bank accounts were much less risky than people with the same income level without direct deposit. In fact, the lack of direct deposit on a reported high income turned out to be a signal of potential fraud.
Different Strokes for Different Folks
Using AI and ML technology also allows credit unions to build different models for different kinds of loans, as opposed to using a one-size-fits-all FICO score. A person seeking a home improvement loan is probably at a different stage of life than someone looking for a loan to buy a used car. Why should they be scored the same way?
Our analysis found that a person’s average credit limits across their accounts were much more important when analyzing whether they would pay off an auto loan versus a home improvement loan. The opposite was true for payment history. It’s a more important metric on a home improvement loan than an auto loan.
Credit Unions Need Help Shrugging off These Misconceptions
There is an opportunity for credit unions to improve their lending practices significantly by analyzing more data and using different models tailored for different kinds of loans. It is going to take work for them to move past fifty years of traditional underwriting muscle memory, but the change is worth it if they are to compete with bigger competitors and offer financial solutions to the underserved communities that have been left out of the traditional lending infrastructure for so long.
Regional financial institutions are well-positioned to provide greater financial access in the communities that need it most. It’s core to their mission. But, in order to continue living up to that mission, they need to identify their underwriting blind spots and look for solutions that help them address and correct them. A diverse society can’t be served properly with one-size-fits-all underwriting.
About the Author:
Mike de Vere, CEO Zest AI